Diffusers
Safetensors
OrbitQuantComponentArtifact
orbitquant
quantized
diffusion-transformer
8-bit precision
Instructions to use WaveCut/FLUX.2-klein-4B-OrbitQuant-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/FLUX.2-klein-4B-OrbitQuant-W4A4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/FLUX.2-klein-4B-OrbitQuant-W4A4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Replace artifact with paper-correct codebook v2 and native proof
Browse files- README.md +18 -1
- SHA256SUMS +4 -3
- assets/image_generation_comparison_matrix.webp +3 -0
- benchmark/summary.json +73 -0
- orbitquant_manifest.json +2 -1
README.md
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@@ -70,6 +70,21 @@ Use these settings when comparing this artifact against the BF16 source model or
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- Release-grade paper metrics: not applicable to this extra target.
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- The model card reports artifact-level validation status only.
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## Quantization
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- Method: `orbitquant`
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## Visual Comparison
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## Source
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- Release-grade paper metrics: not applicable to this extra target.
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- The model card reports artifact-level validation status only.
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## Native Validation Evidence
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The compact benchmark summary records native BF16-vs-OrbitQuant evidence for the comparison matrix below. Detailed per-sample generation records are retained outside this compact artifact.
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| Evidence | Value |
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| --- | --- |
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| Comparison matrix | `assets/image_generation_comparison_matrix.webp` |
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| Paired prompt/seed count | `1` |
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| BF16 source generated samples | `1` |
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| BF16 source generated frames | `0` |
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| BF16 source nonempty outputs | `1` |
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| OrbitQuant generated samples | `1` |
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| OrbitQuant generated frames | `0` |
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| OrbitQuant nonempty outputs | `1` |
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## Quantization
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- Method: `orbitquant`
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## Visual Comparison
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The following assets are stored in this artifact and compare the BF16 base generation against the OrbitQuant generation with the same prompt and seed.
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## Source
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SHA256SUMS
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-
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-
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bd85d0a739b4f5ddf2e3a109c8163ab019ba4a7f68ed07b4ff4d3362b41c62a7 model.safetensors
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4cf21005259004e390579286648418945378ef08022e396d14e165ae13672f2e model_index.json
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e4f2fff365e0b0b6019117abb39ba2817c92173521be726e02919d6456929411 orbitquant_codebooks.safetensors
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-
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3a4014dbeee3988e275fcae07cc9312aceab5d14fb06c55f9a6548d1d7e2fa17 orbitquant_rotations.safetensors
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9d0a30e526f2840b3c68f61565e7345602d23175f2765dec60a6319a9bfcaedd prompts.json
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d90798b18112e3a3b6af9e17913426ee6b500a3ab57e04b6df4cedb306a4cdd8 quantization_config.json
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4ee3a41602cf4a031a942065107c7b53d20ad4fb3df8cc191352ca7d3b597552 README.md
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999e5a4fcf8ca82b35e578c14c23e1f6292b083013a22f26dd8654c902788bdc assets/image_generation_comparison_matrix.webp
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72ed292b6339b1b594542959940798c5544e2e8d3cfb55acef446919c6338dd8 benchmark/summary.json
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bd85d0a739b4f5ddf2e3a109c8163ab019ba4a7f68ed07b4ff4d3362b41c62a7 model.safetensors
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4cf21005259004e390579286648418945378ef08022e396d14e165ae13672f2e model_index.json
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e4f2fff365e0b0b6019117abb39ba2817c92173521be726e02919d6456929411 orbitquant_codebooks.safetensors
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661058a8b84a7d5cc223e7d718ced4fd088938eb24e703aeac27b7fb16af9dd5 orbitquant_manifest.json
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3a4014dbeee3988e275fcae07cc9312aceab5d14fb06c55f9a6548d1d7e2fa17 orbitquant_rotations.safetensors
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9d0a30e526f2840b3c68f61565e7345602d23175f2765dec60a6319a9bfcaedd prompts.json
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d90798b18112e3a3b6af9e17913426ee6b500a3ab57e04b6df4cedb306a4cdd8 quantization_config.json
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assets/image_generation_comparison_matrix.webp
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Git LFS Details
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benchmark/summary.json
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"device_transfer_seconds": 0.717645316850394,
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"module_device_transfer_count": 103,
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"metrics": {},
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"published_summary": "compact",
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"raw_generation_records": "local-only"
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}
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"device_transfer_seconds": 0.717645316850394,
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"module_device_transfer_count": 103,
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"metrics": {},
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"native_smoke": {
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"proof_format": "orbitquant-native-smoke-v1",
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"proof_source": "local_compare_native_bundle",
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"comparison_asset_path": "assets/image_generation_comparison_matrix.webp",
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"paired_prompt_seed_count": 1,
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"paired_prompt_seed_keys": [
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[
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"flux2-native",
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"0",
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"simple-object"
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]
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],
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"splits": {
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"original": {
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"records": 1,
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"generated_samples": 1,
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"generated_frames": 0,
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"nonempty_output_count": 1,
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"seeds": [
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"0"
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],
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"prompt_ids": [
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"simple-object"
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],
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"pair_keys": [
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[
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"flux2-native",
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"0",
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"simple-object"
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]
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],
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"native_settings": [
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{
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"frames": null,
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"guidance": 1.0,
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"height": 1024,
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"steps": 4,
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"suite": "flux2-native",
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"width": 1024
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}
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]
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},
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"orbitquant": {
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"records": 1,
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"generated_samples": 1,
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"generated_frames": 0,
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"nonempty_output_count": 1,
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"seeds": [
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"0"
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],
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"prompt_ids": [
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"simple-object"
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],
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"pair_keys": [
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[
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"flux2-native",
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"0",
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"simple-object"
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]
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],
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"native_settings": [
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{
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"frames": null,
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"guidance": 1.0,
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"height": 1024,
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"steps": 4,
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"suite": "flux2-native",
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"width": 1024
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}
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]
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}
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}
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},
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"published_summary": "compact",
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"raw_generation_records": "local-only"
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}
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orbitquant_manifest.json
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]
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},
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"checksums": {
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-
"
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"model.safetensors": "bd85d0a739b4f5ddf2e3a109c8163ab019ba4a7f68ed07b4ff4d3362b41c62a7",
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"model_index.json": "4cf21005259004e390579286648418945378ef08022e396d14e165ae13672f2e",
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"orbitquant_codebooks.safetensors": "e4f2fff365e0b0b6019117abb39ba2817c92173521be726e02919d6456929411",
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]
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},
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"checksums": {
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+
"assets/image_generation_comparison_matrix.webp": "999e5a4fcf8ca82b35e578c14c23e1f6292b083013a22f26dd8654c902788bdc",
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"benchmark/summary.json": "72ed292b6339b1b594542959940798c5544e2e8d3cfb55acef446919c6338dd8",
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"model.safetensors": "bd85d0a739b4f5ddf2e3a109c8163ab019ba4a7f68ed07b4ff4d3362b41c62a7",
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"model_index.json": "4cf21005259004e390579286648418945378ef08022e396d14e165ae13672f2e",
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"orbitquant_codebooks.safetensors": "e4f2fff365e0b0b6019117abb39ba2817c92173521be726e02919d6456929411",
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